This study uses audiovisual narratives to investigate adolescents’ responses to homophobic bullying of Brazilian gay boys. It explores how the gender expression (conforming vs non-conforming) and sexual orientation (gay vs heterosexual) of the victims influence viewers’ support. A sample of Brazilian adolescents (n = 178; 11—16 years-old, M = 13.7, SD = 1.6; 50
Large language models (LLMs) can be used to support software development tasks, e.g., through code completion or code generation. However, their effectiveness drops significantly when considering less popular programming languages such as domain-specific languages (DSLs). In this paper, we propose a generic framework for evaluating the capabilities of LLMs generating DSL code from textual specifications. The generated code is assessed from the perspectives of well-formedness and correctness. This framework is applied to a particular type of DSL, constraint languages, focusing our experiments on OCL and Alloy and comparing their results to those achieved for Python, a popular general-purpose programming language. Experimental results show that, in general, LLMs have better performance for Python than for OCL and Alloy. LLMs with smaller context windows such as open-source LLMs may be unable to generate constraint-related code, as this requires managing both the constraint and the domain model where it is defined. Moreover, some improvements to the code generation process such as code repair (asking an LLM to fix incorrect code) or multiple attempts (generating several candidates for each coding task) can improve the quality of the generated code. Meanwhile, other decisions like the choice of a prompt template have less impact. All these dimensions can be systematically analyzed using our evaluation framework, making it possible to decide the most effective way to set up code generation for a particular type of task.
Multi-agent AI systems improve inference by spawning agents and synthesizing reports. But another agent is not another observation: apparently independent reports may descend from the same evidence, and genuinely independent evidence can produce nearly identical reports. We formalize this as an epistemic Sybil problem. A report Z is an epistemic Sybil extension relative to reports R when I(Theta; Z | R) = 0. No report-only aggregator can generally distinguish replication from independent corroboration: identical reports can warrant different posteriors under unobserved ancestry. A Gaussian shared-root model shows common ancestry does not imply complete redundancy. Repeated extraction adds information toward a source-level ceiling, and correlated extraction errors, which a shared base model can induce among independent agents, lower that ceiling further. We test these predictions with more than 20,000 controlled LLM-agent report and extraction calls on synthetic evidentiary documents. Holding one evidence root fixed while report multiplicity rises from 1 to 32 collapses naive posterior coverage from 0.940 to 0.263. Holding report count fixed while evidence-root multiplicity rises from 1 to 16 closes the gap, and the aggregators are statistically indistinguishable at k = 16. The agent's replicate extraction errors are correlated (gamma_cal = 0.719, estimated out of sample), and a correlated-extraction aggregator restores calibration accordingly. A controlled manipulation isolates representation similarity from evidential ancestry. It changes a report-space deduplication mechanism's mean inferred cluster count by 1.425 (95
This introduction situates Digital Solidarity Economies (DSE) as an analytical and practical framework for reimagining the digital economy through cooperation, mutual aid, and shared ownership. In response to the concentration of power within platform capitalisms, DSE highlight grassroots and institutional initiatives that democratise digital infrastructures and governance. Drawing on traditions of the social and solidarity economy, free and open-source cultures, and feminist and decolonial technoscience, the special issue explores how communities across the world build technological sovereignty from below. The contributions collectively advance a plural understanding of digital solidarity economies by foregrounding infrastructural arrangements, situated practices, and institutional experimentation as key sites through which digital economies oriented toward the reproduction of life are being built.
Context: Interest in diversity in software development has significantly increased in recent years. Reporting on diversity in software projects can enhance user trust and assist regulators in evaluating adoption. Recent AI directives include clauses that mandate diversity information during development, highlighting the growing interest of public regulators. However, current documentation often neglects diversity in favor of technical features, partly due to a lack of tools for its description and annotation. Objectives: This work introduces the Software Diversity Card, a structured approach to document and share diversity-related aspects within software projects. It aims to profile the various teams involved in software development and governance, including user groups in testing and software adaptations for diverse social groups. Methods: We performed a literature review on diversity and inclusion in software development and an analysis of 1000 top-starred Open Source Software (OSS) repositories in GitHub to identify diversity-related information. Moreover, we present a diversity modeling language, a toolkit for generating the cards using that language, and a study of its application in two real-world software projects. Results: Despite the growing awareness of diversity in the research community, our analysis found a notable lack of diversity reporting in OSS projects. Applying the card to real-world examples highlighted challenges like balancing anonymity with transparency, managing sensitive data, and ensuring authenticity. Conclusion: We believe that our proposal can enhance diversity practices in software development, support public administrations in software assessment, and help businesses promote diversity as a key asset.
